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A Visual Analytic Framework for Graphs.

Shaverdian, Anna Arpi

Shaverdian, Anna Arpi

2012

Abstract: Visual Analytics is the science of analytical reasoning through visual
interaction. Graphs are ubiquitous: high-throughput ``omics'' sciences
generate data to study pathways and computer networks use graphs to
analyze communications. Graphs are also particularly amenable to
visual representation. It is no surprise that graph visual analytics
has received a lot of attention as evidenced by the large number of
tools and algorithms developed for this purpose. These systems can be
very informative, but usually constrain the reader in realizing their
full value for the following reasons: they are unable to be scaled or
modified for changing and increasing data and they do not support
missing attributes and uncertainty in datasets. This dissertation
presents several components within a visual analytic framework to
address these problems.
To support scalable and modifiable methods for graph visual analytics,
we introduce a visual analytic graph algebra and its atomic operators,
which include selection and aggregation. We demonstrate an
implementation of the algebra through a plugin on a widely used
visualization tool, Cytoscape. We then use Cytoscape and the algebra
to create visualizations to replicate previous statistical studies on
high-throughput biological datasets. Next, we present optimization
techniques to accelerate the visual exploration and discovery process
for repeat workflows. Within our framework, we present a multi-modal
graph exploration tool, GreenTrellis to address interaction challenges
with large graphs. GreenTrellis links multiple graph signature
scatterplots to characterize the graph at different node localities.
We present several rich signature representations based on different
characteristics and show how they can be used in different analytic
tasks through example and user study. Interacting with real world data
is messy, data collection often results in missing data; furthermore,
the user may have a degree of uncertainty about a desired query. We
develop a pattern-matching based algorithm to predict values for
missing attributes at nodes. Finally, we introduce a probabilistic
framework within the visual analytic algebra that incorporates
uncertainty in the queries and provides a probabilistic assessment of
the likelihood of the final obtained outcomes.